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Label propagation method based on bi-objective optimization for ambiguous community detection in large networks.

Junhai Luo1, Lei Ye2

  • 1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China. junhai_luo@uestc.edu.cn.

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Summary

This study introduces a novel label propagation algorithm for community detection. It optimizes local clustering coefficients with constraints, improving modularity and speed, especially for ambiguous network structures.

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Area of Science:

  • Network Science
  • Data Mining
  • Computational Social Science

Background:

  • Community detection is crucial for understanding network structures and has broad applications.
  • Label propagation is an effective method for community detection in large networks.
  • Local clustering coefficient measures node aggregation, a key factor in community identification.

Purpose of the Study:

  • To optimize community detection by refining the use of local clustering coefficients.
  • To introduce constraints to prevent the formation of overly large communities.
  • To develop a hybrid objective function for an improved label propagation algorithm.

Main Methods:

  • Optimizing two objectives related to the local clustering coefficient.
  • Implementing constraints to control community size.
  • Developing a hybrid objective by linearly weighting two optimized objectives.
  • Guiding label updates in a label propagation algorithm using the hybrid objective.

Main Results:

  • Experimental validation on artificial and real-world networks.
  • Demonstrated superiority in terms of modularity compared to existing methods.
  • Showcased enhanced speed, particularly for networks with ambiguous community structures.
  • Identified an optimal strength for the applied constraint through comparative experiments.

Conclusions:

  • The proposed label propagation algorithm effectively detects community structures.
  • The algorithm offers significant improvements in both modularity and computational speed.
  • It performs exceptionally well in challenging scenarios with ambiguous community divisions.